How can enterprises choose GenAI tools that work for them and create a budget?

Carrie Pallardy, Contributing Reporter

March 12, 2024

9 Min Read
Business growth concept by using AI robot and machine learning technology to analyze data
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Generative AI is the hot topic on everyone’s mind. The possibilities seem endless, and thousands of companies, products, models, and platforms have flooded the market to monetize those possibilities. Big players like OpenAI, Microsoft, Meta, Google, and Amazon, along with many other large companies and myriad startups, are vying for market share in the GenAI space. 

Real-life use cases are emerging across multiple industries. Wendy’s is using Google Cloud’s GenAI at its drive-throughs. Coca-Cola released an AI platform created by OpenAI and Bain & Company as a part of its marketing function. Companies are using GenAI for customer chat applications, to assist with coding, to help write emails, and more.  

Right now, Lee Moore, VP of Google Cloud Consulting, sees enterprises on a spectrum when it comes to GenAI. “At one end of the spectrum, you’ve got customers who are almost paralyzed, whether it’s their internal processes, their concerns around data privacy, or other things that you read about,” he explains. 

In the middle of the spectrum, enterprises are experimenting with GenAI but not yet investing a significant amount. And then, there are enterprises that are seriously investing in real-life use cases, according to Moore.  

Regardless of where an enterprise is in its approach to GenAI, there seems to be no question that the technology is here to stay. But how can enterprise leaders work through the hype cycle to set a budget for this technology and select the right solutions for their organizations? 

Related:Thomson Reuters on Using GenAI to Augment the Professional Workforce

Making the Business Case 

GenAI promises a lot of things: improved efficiencies, cost savings, happier employees and customers. But how can that technology do that for your enterprise? Before making a significant investment in any technology, enterprise leaders need to understand the business case.  

As C-suite leaders and boards talk about GenAI, they can ask questions about “…what their priorities are from a cost-saving, revenue-driving, reduction of risk, better customer experience [perspective], and what the right stack is to help serve … their enterprise outcomes,” says Ryan Martin, partner and AI global lead at Infosys Consulting, a management and IT consulting company.  

Being able to have that discussion requires an investment in learning. GenAI technology is evolving so quickly that it demands enterprise leaders constantly learn so they don’t fall behind. “In my team, I have allocated 10% of everybody’s time this year to learning because of the speed of which this is going,” Moore shares.  

Related:Special Report: What's Next for the GenAI Market in 2024?

As part of that learning process, Moore’s team will participate in hackathons or competitions. “The team, in a couple of days, came up with the solution that would pretty much automate the generation of our services contracts with our customers,” he shares. “We get to 80 odd percent completion in minutes. And then the human in the loop gets in there to finish off the rest of the contracting.” 

Business function leaders can explore how GenAI can solve challenges for their teams.  

Demonstrating how the technology can solve a specific problem can create a foundation for an initial investment that can be scaled. “It always starts with defining your problem statement and then looking at what are the host of tools you have to solve that,” Prasad Ramakrishnan, CIO at Freshworks, a cloud-based SaaS company, tells InformationWeek.  

Selecting Tools and Partners 

With a business case in mind, enterprise leaders need to sift through a sea of options to find the right GenAI tools and/or partners.  

“There is a massive proliferation of startups [with] various niche capabilities, and some of them are just out there trying to win budgets and take advantage of the hype, while others have been built on research,” says Erik Brown, senior partner, AI and product engineering at digital services firm West Monroe Partners.  

Related:Experts Ponder GenAI’s Unprecedented Growth and Future

Just how much the GenAI market will change in the coming years is hard to predict. With so many companies offering solutions, it is unlikely that all of them will become long-time players in the space. “You can’t even keep track of how many are popping up on a regular basis. And my suspicion is within six months half [of] them won’t be there anymore,” says Ryan Worobel, CIO at LogicMonitor, a cloud-based infrastructure monitoring platform.  

Considering a company’s funding position and its likelihood of longevity can help narrow down the options. Enterprises can also examine what their existing technology partners are doing with GenAI to see if there is a potential fit. “It really is [about] figuring out who are the vendors you trust that you’re working with and … the vendors you have who you truly consider a partner in the process that wouldn’t jeopardize their own relationship with the company just to start throwing capabilities out there,” Worobel explains.  

While many enterprises are in the experimentation phase, it is important to consider the big picture. AI is a broad category of which GenAI is a piece. How does a potential tool fit in an enterprise’s overall tech stack?  

“I would say a potential red flag is one [a tool] that’s maybe too myopic in its focus … [it] needs to be able to integrate with other AI technologies in the stack,” says Martin.   

Moore argues for a platform approach. “You don’t want to get locked into any particular model or tool because there are now hundreds if not thousands of them today,” he says.  

Choosing a GenAI solution also involves an element of risk management. Martin points out that enterprises can learn lessons from technologies that have emerged and generated massive hype and demand in the past.  

“If you don’t [have] guardrails and some level of governance within your own organization, you’re going to end up with thousands of one-off, new implementations that add risk to the organization, add overhead in terms of management, add pain that I think can be avoided if you do it intelligently and with the right governance structure,” he cautions.  

Setting a Budget 

How much an enterprise spends on GenAI this year, next year, and beyond will depend on a multitude of factors: its industry, its size, its maturity, its target use cases. “We’ve seen success in integrating with an existing AI model … an investment of a $150,000 to $200,000. We’ve also built highly complex custom models into products that take [an] investment of $1 million to $2 million,” says Brown.  

Where do enterprises want to put their dollars toward GenAI? For some, it might make sense to focus on external partnerships and solutions. For others, dollars might be spent on internal R&D. Many enterprises will be budgeting for both.  

“It’s going to be far more predictable to think about how you set a blanket budget for the use of licensed-embedded AI tools and enterprise software like Microsoft Office,” says Brown. He expects that budgeting for building GenAI and other forms of AI into custom internal products and workflows will likely be the bigger investment. “But I think that’s where the most compelling opportunity is going to be moving forward,” he contends. 

Organizations can approach setting a budget for GenAI in different ways. Worobel shares that his team is taking lessons from the advent of cloud technology. They set aside a percentage of the IT budget into an innovation bucket, which they dipped into to test cloud. Now, Worobel and his team are looking at an innovation bucket for GenAI.  

“We just put a bucket of dollars kind of on the side in the overall AOP [annual operating planning],” Worobel shares. “We don’t really have [it] earmarked for anything yet, and what we’re doing as we come upon different ideas and look at different components and capabilities, we’ll make decisions or whether or not we fund it.” 

Choosing what to invest in goes back to the business use case. What will a particular solution deliver in terms of increased productivity or efficiency? Moore recommends targeting a specific improvement and then deciding what piece of the budget is required to achieve it. 

“I would be asking my team to come back to me with a plan to deliver a certain percentage improvement in efficiency and output,” he outlines. “Let’s say, 5% or 10% in the first year, with an expectation it would grow beyond that and to give me the budget request to achieve that.” 

Tracking ROI 

Enterprise leaders want to know that GenAI is generating returns. Tracking its ROI does not have to be radically different than doing so for any other technology investment. Select KPIs and monitor them. Is that tool improving, for example, coder productivity? Is it increasing customer conversion rates?  

Starting with a small pilot project can help enterprise leaders prove the use case for a GenAI solution and then scale. “If you isolate the people using the tools to start and you set them on kind of clear goals of how to use the tools, you can measure productivity pretty quickly and then figure out how to expand that beyond that small pilot group,” says Brown.  

Enterprise leaders spearheading a GenAI use case test also need to track utilization of the tool in question. “You need to track app deployments, app utilization, and [if they are] using all the features,” says Ramakrishnan. If users aren’t adopting the tool or are only using a fraction of the features the enterprise is paying for, there are dollars being wasted.  

Staying Flexible 

Budgeting for GenAI is going to be a learning experience. Enterprise leaders are going to need to make adjustments as they learn which external partners and tools and internal development initiatives drive progress toward their business goals.  

In anticipation of the rapid pace at which GenAI is evolving, organizations can leave space in their budgets to adapt as changes arise.  

“I try to keep somewhere between 15% and 20% of my budget unallocated as I enter the year, which can be tough to do in tight budget environments,” Worobel shares. “But as long as you have that close partnership with your CFO and you can have an agreement that we’re not going to needlessly spend it and if we don’t need it, we’ll give it back.” 

Enterprise leaders can also track how GenAI generates cost savings and how those cost savings can potentially impact the overall budget. Ramakrishnan emphasizes the importance of app rationalization as a potential avenue to cost savings. How can a new GenAI tool potentially streamline a business function and pave the way for retiring now unnecessary tools?  

This year, for many enterprises, is a year of experimentation. But GenAI is poised to soon move beyond the hype cycle, bringing more concrete, real-life use cases to bear.   

“I think this year is all about actually justifying the spend [on] enterprise supplementary tools, embedded AI tools, [whereas] the investment in engineering around data science and artificial intelligence for the general enterprise is going to grow more significantly going into next year,” says Brown.  

While each enterprise has to lay out its own path for adopting a new technology, GenAI will not be ignored.  

“I think the lesson from the dot-com boom 20 years ago, people didn’t fully believe in the possibilities. Here, the possibilities are going to become real very, very quickly,” says Moore.  

 

About the Author(s)

Carrie Pallardy

Contributing Reporter

Carrie Pallardy is a freelance writer and editor living in Chicago. She writes and edits in a variety of industries including cybersecurity, healthcare, and personal finance.

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